All methods shared the same backbone architecture. For the core contribution of our work, we used Conv4 architecture consisting of 4 convolutional layers with 64 channels (padding 1), interleaved by batch normalization [1]}, ReLU activation function, and max-pooling (kernel size 2, and stride 2) [2]}. Relation Network used max-pooling only for the last 2 layers of the backbone to account for the Relation Module. The Relation Module consisted of two additional convolutional layers, each followed by batch norm, ReLU, max-pooling).
